Method and Apparatus for Maintaining a Background Image Model in a Background Subtraction System Using Accumulated Motion

ABSTRACT

Methods and apparatus are provided for maintaining a background image model in a background subtraction system using accumulated motion. A background image model is maintained by obtaining a map of accumulated motion; and adjusting the background image model based on the map of accumulated motion. The map of accumulated motion may be obtained, for example, based on one or more of motion field images; stability maps; frame differences; or information from a background subtraction system. Objects can be added to or removed from the background model or the background model can be otherwise updated One or more pixels from an image are added to the background image model if a stability measure for the one or more pixels satisfies a predefined criteria. A portion of the background image model can be invalidated in regions where the map of accumulated motion exceeds a predefined threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/345,854, filed Feb. 2, 2006, incorporated by reference herein

FIELD OF THE INVENTION

The present invention relates generally to imaging processingtechniques, and, more particularly, to techniques for generating andmaintaining background model images in a background subtraction system

BACKGROUND OF THE INVENTION

Background subtraction is a common technique for detecting movingobjects in a largely stationary environment. Generally, backgroundsubtraction techniques compare a current image against a reference“empty” image and note regions of change. Such differences generallycorrespond to the moving foreground objects of interest for applicationssuch as surveillance. However, a background model must first be createdand then maintained.

A background model can be created, for example, by memorizing an “empty”image where there are no visible objects. However, this method is notgenerally applicable because it is difficult to ensure that a scene isclear of all moving objects, especially if it is being remotelymonitored. Another technique monitors an incoming video stream formotion energy by subtracting adjacent frames When the overall energy islow enough, an input frame is captured and thereafter used as thebackground model. Unfortunately, if a moving object, such as apedestrian, momentarily pauses, a background image might be acquiredwhich erroneously includes this temporarily immobile object.

Conversely, moving objects may enter a scene and then stop moving (e.g.,a parked car). Similarly, if a person enters a room and then becomesimmobile, such as taking a nap, the person persists as a detectedobject. In many cases, such objects would be better interpreted as apart of the “new” background. Moreover, if a person is present when thereference “empty” image is acquired, the person will be detected once heor she starts to move across the scene. However, even after the personhas completely exited the scene, a “hole” where the person wasoriginally positioned will likely continue to be perceived as an object

The background updates can be significant events in themselves. If anitem is introduced into the scene (such as a briefcase carriedsurreptitiously by some agent), the item will also be marked as part ofthe foreground despite having no motion itself. A related situation iswhere some object that was part of the original reference image (e.g., alaptop computer) is removed from the scene. There will be a difference“hole” left behind in this case that is not only non-moving, but alsonot a solid object. There are several methods that can be used to locatenon-moving regions in a background subtraction system, but it isdifficult to classify such regions as abandoned objects, removedobjects, state changes, or another phenomena.

The region type can be determined, for example, by comparing the pixelpattern in the region to the template of some known object (e.g., acomputer monitor). If there is a non-moving foreground region and theassociated pixels match the template, then a deposit event has occurred,otherwise a removal event is recorded. However, this approach requiresthat the system have a number of templates for each kind of object itcares about. Typically, such models must be manually entered, or anoperator has to at least mark the boundaries of some region. A relatedapproach requires that the system know something about the backgroundnear the non-moving region. If, for instance, the room had uniformlygreen walls, then the interior of the non-moving region could beexamined to see if it was green or not (implying a removal or a deposit,respectively). Again, this is not a general purpose solution and worksbest if the characteristics of the environment can be chosen at will(like applying green paint).

Updating the background model is particularly important for environmentsin which lighting changes over time (e g, outdoors). Otherwise, lightingchanges, such as the sun coming out from behind clouds, can cause largeareas of the image to be falsely declared as foreground as they becomebetter illuminated. Updating the background model is often done byslowly blending in newly acquired images with the old model. However, ifthe blending rate is fast and applied to the whole image, moving objectsstart to leave ghostly trails across the background model. Suchanomalies can cause the system to both falsely detect nonexistentobjects and miss detecting some valid objects. If, on the other hand,the blending rate is set very slow to reduce this effect, the systemmight not adapt quickly enough to the types of lighting changes that arepresent. Another updating option is to periodically reinitialize thebackground model from scratch using the current video frame This mightbe done, for instance, either on a regular schedule or when too large aportion of the image is marked as foreground While this approachprevents ghosting, it still suffers from the same initial modelacquisition problems as described above

Background blending, as discussed above, will eventually “erase” allstationary objects as well as “holes” left by removed objects.Unfortunately, this method tends to erase objects by shrinking them,leaving invalid partial objects as the regions are absorbed into thebackground. Moreover, background blending can also leave “ghost” objectsbehind if, say, a moving person lingers too long in one area. Thereexist more sophisticated systems that model the intensity at each pixelas one of several Gaussian distributions. These systems have betterimmunity to the “ghosting” problem, but the decision to switch from oneGaussian model to another is typically made independently for eachpixel. This leads to objects or holes “sparkling” out, with incorrectragged objects detected during the transition

As apparent from the above-described deficiencies with conventionaltechniques for generating and updating a background model, a need existsfor methods and apparatus for improved techniques for generating andupdating a background model A further need exists for methods andapparatus for visual background subtraction that address each of theabove-identified problems using one or more software preprocessingmodules.

SUMMARY OF THE INVENTION

Generally, methods and apparatus are provided for maintaining abackground image model in a background subtraction system usingaccumulated motion According to one aspect of the invention, abackground image model is maintained by obtaining a map of accumulatedmotion; and adjusting the background image model based on the map ofaccumulated motion In one implementation, a counter is maintainedindicating a degree of motion in a portion of a sequence of images.

The map of accumulated motion may be obtained, for example, based on oneor more of motion field images; stability maps; frame differences; orinformation from a background subtraction system. Based on the map ofaccumulated motion, objects can be added to or removed from thebackground model or the background model can be otherwise updated

According to another aspect of the invention, one or more pixels from animage are added to the background image model if a stability measure forthe one or more pixels satisfies a predefined criteria. In addition, aportion of the background image model can be invalidated in regionswhere the map of accumulated motion exceeds a predefined threshold. Aportion of an image can be copied, for example, to the background imagemodel if a stability measure satisfies a predefined criteria.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of a background model processingsystem incorporating features of the present invention;

FIG. 2 is a flow chart describing an exemplary implementation of abackground building method that may be employed by the backgroundbuilding module of FIG. 1;

FIG. 3 is a flow chart describing an exemplary implementation of abackground invalidation method that may be employed by the backgroundinvalidation module of FIG. 1;

FIG. 4 is a flow chart describing an exemplary implementation of abackground region removal process that may be employed by the backgroundregion removal module of FIG. 1; and

FIG. 5 is a flow chart describing an exemplary implementation of aforeground blending module that may be employed by the foregroundblending module of FIG. 1.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention provides methods and apparatus for maintaining abackground image model in a background subtraction system using a map ofaccumulated motion. As discussed hereinafter, the map of accumulatedmotion can be obtained, for example, based on frame differences or froma background subtraction system. The accumulated motion can be employedto add or remove objects from the background model or to update thebackground model. The background model can be updated on apixel-by-pixel basis or at a larger object level.

FIG. 1 is a schematic block diagram of a background model processingsystem 100 incorporating features of the present invention. Thebackground model processing system 100 maintains a background imagemodel 140 using a map of accumulated motion. As discussed further below,the background model processing system 100 performs image differencedetection at stage 195, for example, to detect one or more objects in animage, and employs one or more processing modules 200, 300, 400, 500,each discussed below in conjunction with FIGS. 2 through 5 respectively.The processed image may be obtained, for example, from a remote camera110, and the images generally have undergone an image compression 120.The compressed image may be received, for example, over a transmissionmedium or channel 125, such as a wired or wireless link.

According to one aspect of the invention, one or more auxiliary images,referred to as motion field images 150, are maintained that indicate thesource of frame-by-frame motion, such as where there has been recentmotion in the image, differences and edges. As discussed hereinafter,the motion field images 150 are used to incrementally build,progressively update, and intelligently heal the background model. Inaddition, one or more stability maps 155 maintain historical informationabout the motion field images 150, such as non-motion frame counts foreach pixel.

The motion field images 150 can be obtained, for example, by looking fordifference over time between two images (such as binary, grayscale, orcolor). Based on the regions where a significant change is detected, thecorresponding pixels are either incremented by a large value (butclipped to some maximum value, such as 255) or decremented by a certainamount (but prevented from assuming negative values). The particularmethods below all use this core processing technique but differ in howthey determine where pixels are incremented versus decremented, and inhow the resulting map of motion (or non-motion) is interpreted.

The creation of the background model 140 starts by looking for regionsthat have been stable for a sufficient period of time. The correspondingpixels of the input image are then copied to create a partial backgroundimage As the offending object(s) moves away from various portions of thescene, corresponding parts of the background model 140 will beincrementally built. A separate background validity image 160 ismaintained in accordance with another aspect of the invention to controlwhere background subtraction can legitimately be performed (e.g., wherethe background has been built and remains valid versus still unknown)

According to one aspect of the invention, a similar motion map is usedto control blending updates. The background model 140 is only changed inregions that have been stable for a sufficient duration. Also, regionsthat exhibit consistently high motion over long periods of time (e.g,bushes shaking in the wind) can be removed from the background validityimage 160 and thereby reduce the number of false positive objectdetections.

The incremental motion-based technique allows rapid building of at leastpartial background models 140 (and hence the creation of at leastpartially valid background subtraction systems) despite the presence ofmoving objects. The use of a similar technique for background updatingallows a fast blending rate to combat environmental lighting changes,but keeps foreground objects from being inadvertently mixed into themodel.

A variation on the proposed technique treats detected objects asconnected regions and decides whether to “push” each such region intothe background model 140 based on a visual motion history. That is, itmaintains a similar motion field map for the interior of detectedforeground objects. Once a decision is made, the whole object can beremoved within a single frame time, since regions are employed there areno confusing partial objects. Furthermore, the removal decision is basedon examining the internal motion of an object. This helps prevent theintroduction of “ghost” objects (and related “holes” when they aresubsequently removed). For instance, suppose a person stops to talk to acolleague in the hall. Although the overall outline of the regiondescribing the person remains constant for a long time, the internalfidgeting and gesticulation typically encountered will keep this object“alive” and prevent its merging with the background.

When a “healing” operation is performed, the category of the underlyingreal-world event can be determined by examining the edge informationassociated with an object. The classification system examines theperiphery of non-moving regions in both the current image and thereference image to determine if a contour has been added or deleted. Adeposited object will typically have a detectable intensity boundaryaround it A “hole,” by contrast, will just expose the underlying texturein the environment. There will be no strong correlation between theregion and the observable contour fragments. The proposed method notonly tallies the amount of contour added or deleted, but also measuresthe fraction of the boundary area changed in order to make its decision.

The present invention allows stationary detected regions in theforeground to be differentiated into distinct types rather thanrequiring them all to be treated in the same manner. The presentinvention can accomplish this differentiation without iconic objectmodels and with minimal constraints on the environment (e.g., somemaximum degree of texture that can be tolerated in general)

Background Building Module

According to one aspect of the invention, a background building module200, discussed in conjunction with FIG. 2, generates a background imagemodel 140 using a map of accumulated motion. In the exemplaryimplementation, the map of accumulated motion is obtained based on framedifferences. The background building module 200 accumulates stillness inorder to add objects to the background model 140 The background buildingmodule 200 updates the background model 140 on a pixel-by-pixel basis.

FIG. 2 is a flowchart describing an exemplary implementation of aprocess implemented by the background building module 200. As shown inFIG. 2, the background building module 200 initially builds a motionimage 150 during step 210 for the image. In one embodiment, this isaccomplished by converting the incoming video into a monochrome formatand subtracting adjacent frames. This difference image is then smoothedslightly (for example, with a 3×3 mask) and, if increased noise immunityis desired, averaged with the smoothed difference image derived for theprevious frame. A predefined threshold is then applied to the resultingpreliminary motion image 150 at some detection value (e g., an intensitydifference of 10 for pixels in the range 0 to 255) and a morphology-likeoperator is used to remove small noise areas.

Thereafter, a stability map 155 for the scene is generated during step220. The stability map 155 keeps non-motion frame counts for each pixeland is initialized to all zeroes at the start of the video sequence. Toform the stability map image 155, the initial binary motion image 150 isfirst combined with any detected foreground mask by logically ORing thetwo images together on a pixel-by-pixel basis This combined image canthen be “fattened up” (typically by 9 pixels in a 160×120 image) usinglocal average operators so that areas near detected motion or foregroundobjects also fall under the resulting motion image. In general, all thepixels of the stability image 155 are incremented on each successiveframe However, where the motion image is active the counts are zeroedinstead.

Finally, the stability map 155 is thresholded at some count during step230 (e.g., 30 for video at 30 frames per second). The incoming image iscopied directly to the background image 140 during step 240, but onlyfor those pixels that are active in this thresholded image but not yetmarked as valid When this copying occurs, the corresponding pixel in thebackground validity image 160 is set appropriately to indicate properinitialization (and prevent later overwriting).

Background Invalidation Module

According to another aspect of the invention, a background invalidationmodule 300, discussed in conjunction with FIG. 3, removes portions ofthe background image model 140 using the accumulated motion. In theexemplary implementation, the map of accumulated motion is obtainedbased on frame differences. The background invalidation module 300accumulates motion activity in order to remove objects from thebackground model 140. The background invalidation module 300 updates thebackground model 140 on a pixel-by-pixel basis

To detect persistent motion of background objects, such as wind rufflingthe leaves of a tree, a motion image 150 is built by examining thetexture of the foreground versus the background. In one exemplaryembodiment, three 3×3 pixel edge operators are applied to the imagesduring step 210: the Sobel horizontal mask, the Sobel vertical mask, anda center-surround (i e., top-hat) mask During step 320, differences arethen computed in each texture modality (e g., horizontal in the currentimage versus horizontal in the background image), the difference valuesare then converted to absolute values, and then combined into a weightedsum to Form Motion Field Image 150. Edges are used to make the resultingdifference map very sensitive to small scale motion.

A test is performed during step 330 to determine where the computeddifference is above a significance threshold. When the computeddifference is above the significance threshold, a corresponding pixel ofstability map 155 is incremented by a fixed amount (e.g., 32) duringstep 340. In regions where there are currently no differences, thecorresponding non-zero pixel of the stability map 155 is decremented byone during step 350. This image essentially keeps track of where therehas been high (possibly intermittent) motion. The background validityimage 160 is then invalidated during step 360 in regions where thestability map 155 exceeds a predefined threshold.

To provide an adequately long averaging interval it may be advantageousto only update the motion image every N frames (e.g., 4). Note that themotion image 150 from the background invalidation module 300 can bemaintained for all regions of the image, even where the background iscurrently invalid. Thus, for example, if the wind stops blowing, theregion corresponding to the bush could “settle down” and once againrejoin the rest of the valid background image 140.

Background Region Removal Module

According to another aspect of the invention, a background regionremoval module 400, discussed in conjunction with FIG. 4, altersportions of the background image model 140 using the accumulated motionIn the exemplary implementation, the map of accumulated motion isobtained based on frame differences. The background region removalmodule 400 accumulates stillness in order to alter objects in thebackground model 140 The background region removal module 400 updatesthe background model 140 on an object level.

As shown in FIG. 4, the background region removal module 400 maintainsthe stability map 155 (i.e., a “quiescence” image) for the scene duringstep 410. Generally, each pixel in this image 155 is incremented by oneon each successive frame (or other regular interval), up to some maximumsaturation value (typically 255). However, if motion is detected at apixel, its quiescence value is reset to zero instead in the stabilitymap 155 The motion value for each pixel is derived from a motion mapimage 150.

In one embodiment, this motion image 150 is implemented as thepixel-wise difference of two successive monochrome video frames.Morphological operations (implemented using local average operators) arethen optionally performed on this raw motion image to eliminatepotential noise-like responses and generate a binary version. Finally,the initial binary version is “fattened up” (typically by 9 pixels)using additional morphology-like operations to yield the final binarymotion image 150. This image 150 encodes the decision of whether therehas been recent motion at or around each pixel.

The background region removal module 400 then interprets the quiescenceimage 155 with respect to a binary foreground mask received from theimage difference detector 195. The foreground mask is first broken intoconnected components during step 420, then all the pixel locations ineach component are checked against the quiescence image 150. The minimumvalue of quiescence encountered is recorded for each component. Thus,even if only one small part of an object is moving, the whole objectwill inherit that motion value.

If it is determined during step 430 that the quiescence value for acomponent is above a predefined threshold (such as 150), it is proposedas a region to be “healed” during step 440 Healing is accomplished, forexample, by copying directly to the background model that portion of thecurrent image corresponding to the pixels of the selected component.Generally, the quiescence image 155 remains untouched. If the quiescencevalue for a component is not above a predefined threshold during step430, program control terminates.

However, when a region is identified as a candidate for healing, insteadof being automatically assimilated, the region can instead be proposedto some higher level of processing This higher level might have accessto additional information about the object region, such as whether itwas a deposited object, removed object, or an interesting object beingactively tracked. Depending on the circumstances, this higher levelmight veto the healing of the region for the time being. In this case,the background model 140 remains unchanged but the quiescence values forall the associated pixels are initialized to zero. This prevents thesystem from proposing to heal the exact same region on the very nextframe.

Region Classification

The above described background region removal module 400 can besupplemented by a region classification system that can classify anobject that is removed from (or added to) the background model 140.

The region classification system works by first generating a mask forthe boundary around a specified stationary foreground region. In oneimplementation, the region classification system creates a spatiallyexpanded version of the binary object using local average operators, andanother spatially shrunken version also derived from local averageoperations. The two versions are then differenced to find the pixelsthat are in the fat version but not in the skinny version. The resultingmask marks the pixels that are near the boundary of the specifiedobject. In one embodiment, this ring mask is about 5-7 pixels wide.

Next, a determination is made for each portion of the boundary ringimage about whether there are any edges there. In one embodiment,intensity edges are first computed for both the current image and thereference background image. This can be accomplished, for example, byconvolving 3×3 pixel Sobel masks with monochrome versions of each image.The resulting magnitude responses are then thresholded above someminimum value (like 30) and combined with the spatial ring mask using alogical AND operator. Finally, the gated edges are again smeared by amorphology-like operator so they are approximately as thick as the ringin the boundary mask image, then ANDed back with the original ring mask.Fattening the edge responses in this way compensates for the slightshifting of edge responses under different lighting conditions, andhelps prevent counting complex parallel edges more heavily than simpleedges.

Finally, the two gated edge images are subtracted to form a ternary(3-valued) image showing where contour has been added, removed, orremained the same (i.e, either both images had edges there, or bothimages were smooth). Note that this spatial subtraction directly linksedge events to specific locations (as opposed to comparing just thetotal number of edge pixels in the respective gated ring images, where athicker edge in one area might compensate for the lack of any edge inanother place).

The number of added pixels and subtracted pixels are then totaledseparately and compared to the overall area of the ring mask. If theoverall ring area is below a threshold value (e.g., 100 pixels), anindeterminate state is flagged. Otherwise, when the amount of addedcontour is above some minimum fraction of the total ring area (typically7%) and is also greater than some factor (typically 1.5×) times theamount of contour removed, an object deposition event is reported.Conversely, when the count of subtracted contour satisfies similarconditions, an object removal event is declared. If, instead, theamounts of contour added or subtracted are both small or comparable, astate-change event is reported.

Foreground Blending Module

According to another aspect of the invention, a foreground blendingmodule 500, discussed in conjunction with FIG. 5, alters or updatesportions of the background image model 140 using the accumulated motionIn the exemplary implementation, the map of accumulated motion isobtained based on foreground objects. The foreground blending module 500accumulates stillness in order to alter or update objects in thebackground model 140. The foreground blending module 500 updates thebackground model 140 on a pixel-by-pixel level It is noted that theforeground blending module 500 employs the same motion field images 150and stability maps 155 as the background building module 200.

FIG. 5 is a flowchart describing an exemplary implementation of aprocess implemented by the foreground blending module 500. As shown inFIG. 5, the foreground blending module 500 initially builds a motionimage 150 during step 510 for the image In one embodiment, this isaccomplished by converting the incoming video into a monochrome formatand subtracting adjacent frames. This difference image can then besmoothed slightly (for example, with a 3×3 mask) and, if increased noiseimmunity is desired, averaged with the smoothed difference image derivedfor the previous frame. A predefined threshold is then applied to theresulting preliminary motion image 150 at some detection value (e g, anintensity difference of 10 for pixels in the range 0 to 255) and amorphology-like operator is used to remove small noise areas.

Thereafter, a stability map 155 for the scene is generated during step520 The stability map 155 keeps non-motion frame counts for each pixeland is initialized to all zeroes at the start of the video sequence. Toform the stability map image 155, the initial binary motion image 150can first be combined with any detected foreground mask by logicallyORing the two images together on a pixel-by-pixel basis. This combinedimage can then be “fattened up” (typically by 9 pixels in a 160×120image) using local average operators so that areas near detected motionor foreground objects also full under the resulting motion image Ingeneral, all the pixels of the stability image 155 are incremented oneach successive frame. However, where the motion image is active thecounts are zeroed instead

Finally, the stability map 155 is thresholded at some count during step530 (e.g., 30 for video at 30 frames per second). To update thebackground model 140 (S) over time, the current image (I) isfractionally blended into the background model 140 For example,

S′=(1−f)*S+f*I.

for a blending coefficient, f. However, this blending only takes placewhere the stability map 155 is above the predefined threshold (such as30 for video at 30 frames per second, as above). Yet, for very lowvalues of the blending coefficient, f, this can potentially causesignificant loss of detail if both the input and background image arerestricted to 8 bit pixels. Therefore, this blending can be performed ata rate that is less than every frame, but rather at every n^(th) (e.g.,3rd) frame. This allows a more reasonable blending factor (e.g., 10%) tobe used to simulate the effects of a smaller one (e g., 3%).

System and Article of Manufacture Details

As is known in the art, the methods and apparatus discussed herein maybe distributed as an article of manufacture that itself comprises acomputer readable medium having computer readable code means embodiedthereon. The computer readable program code means is operable, inconjunction with a computer system, to carry out all or some of thesteps to perform the methods or create the apparatuses discussed herein.The computer readable medium may be a recordable medium (e.g., floppydisks, hard drives, compact disks, or memory cards) or may be atransmission medium (e.g., a network comprising fiber-optics, theworld-wide web, cables, or a wireless channel using time-divisionmultiple access, code-division multiple access, or other radio-frequencychannel). Any medium known or developed that can store informationsuitable for use with a computer system may be used. Thecomputer-readable code means is any mechanism for allowing a computer toread instructions and data, such as magnetic variations on a magneticmedia or height variations on the surface of a compact disk.

The computer systems and servers described herein each contain a memorythat will configure associated processors to implement the methods,steps, and functions disclosed herein. The memories could be distributedor local and the processors could be distributed or singular. Thememories could be implemented as an electrical, magnetic or opticalmemory, or any combination of these or other types of storage devices.Moreover, the term “memory” should be construed broadly enough toencompass any information able to be read from or written to an addressin the addressable space accessed by an associated processor. With thisdefinition, information on a network is still within a memory becausethe associated processor can retrieve the information from the network.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A method for maintaining a background image model, comprising:obtaining a map of accumulated motion; and adjusting said backgroundimage model based on said map of accumulated motion.
 2. The method ofclaim 1, wherein said step of obtaining a map of accumulated motionfurther comprises the step of obtaining one or more motion field images.3. The method of claim 1, wherein said step of obtaining a map ofaccumulated motion further comprises the step of obtaining one or morestability maps.
 4. The method of claim 1, wherein said step of obtaininga map of accumulated motion further comprises the step of maintaining acounter indicating a degree of motion in a portion of a sequence ofimages.
 5. The method of claim 1, wherein said map of accumulated motionis obtained based on frame differences.
 6. The method of claim 1,wherein said map of accumulated motion is obtained based on informationfrom a background subtraction system.
 7. The method of claim 1, whereinsaid adjusting step further comprises the step of adding an object tosaid background model.
 8. The method of claim 1, wherein said adjustingstep further comprises the step of removing an object from saidbackground model.
 9. The method of claim 1, wherein said adjusting stepfurther comprises the updating said background model.
 10. The method ofclaim 1, wherein said adjusting step further comprises the updating saidbackground model on a pixel-by-pixel basis
 11. The method of claim 1,wherein said adjusting step further comprises the updating saidbackground model on an object level
 12. The method of claim 1, furthercomprising the step of adding one or more pixels from an image to saidbackground image model if a stability measure for said one or morepixels satisfies a predefined criteria.
 13. The method of claim 1,wherein said adjusting step further comprises the step of invalidating aportion of said background image model in regions where said map ofaccumulated motion exceeds a predefined threshold.
 14. The method ofclaim 1, wherein said adjusting step further comprises the step ofcopying a portion of an image to said background image model if astability measure satisfies a predefined criteria.
 15. The method ofclaim 1, wherein said adjusting step further comprises the step ofupdating said background image model over time if a stability measuresatisfies a predefined criteria.
 16. The method of claim 1, furthercomprising the step of determining whether an object has been added toor deleted from an image by examining edge information associated withan object.
 17. The method of claim 16, wherein an added object has adetectable intensity boundary and a removed object exposes an underlyinghomogeneous legion in an environment.
 18. A system for maintaining abackground image model, comprising: a memory; and at least oneprocessor, coupled to the memory, operative to: obtain a map ofaccumulated motion; and adjust said background image model based on saidmap of accumulated motion.
 19. The system of claim 18, wherein saidprocessor is further configured to obtain one or more of motion fieldimages and stability maps.
 20. The system of claim 18, wherein saidprocessor is further configured to maintain a counter indicating adegree of motion in a portion of a sequence of images.
 21. The system ofclaim 18, wherein said map of accumulated motion is obtained based onone or more of frame differences; or information from a backgroundsubtraction system.
 22. The system of claim 18, wherein said processoris further configured to add an object to said background model, removean object from said background model or update said background model.23. The system of claim 18, wherein said processor is further configuredto add one or more pixels from an image to said background image modelif a stability measure for said one or more pixels satisfies apredefined criteria.
 24. The system of claim 18, wherein said processoris further configured to invalidate a portion of said background imagemodel in regions where said map of accumulated motion exceeds apredefined threshold.
 25. The system of claim 18, wherein said processoris further configured to copy a portion of an image to said backgroundimage model if a stability measure satisfies a predefined criteria 26.The system of claim 18, wherein said processor is further configured toupdate said background image model over time if a stability measuresatisfies a predefined criteria.
 27. An article of manufacture formaintaining a background image model, comprising a machine readablemedium containing one or more programs which when executed implement thesteps of: obtaining a map of accumulated motion; and adjusting saidbackground image model based on said map of accumulated motion.